Evolutionary design of Self-Organizing Fuzzy Polynomial Neural Networks for modeling and prediction of NOx emission process

In this study, we proposed genetically dynamic optimized self-organizing fuzzy polynomial neural network with information granulation based FPNs (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. We illustrate the performance of the network and elaborate on its development by experimenting with data coming from the NOx emission process of a gas turbine power plant. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling